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Background/Objectives: In regions of Africa with a high prevalence of malaria, pregnant women in their first or second trimester should be administered intermittent preventive treatment in pregnancy (IPTp). However, infants may contract malaria despite the IPTp therapy that their mothers have received. The objective of the present investigation was to assess the symptoms and various treatments for neonatal malaria. Methods: Entropy weight and TOPSIS were used to achieve the study goal. The TOPSIS multi-attribute decision-making system was used to assess newborn malaria symptoms and select the optimal treatment, even for mothers receiving IPTp medication during pregnancy. The entropy weight approach calculated TOPSIS attribute weights. The present research used UNICEF data for 14 African nations in 2023. Results: The results indicated that neonates whose mothers received IPTp therapy ultimately contracted malaria, with diarrhea being the primary symptom. It is important to note that health providers administer a combination of zinc and oral rehydration solution (ORS) to infants as the most effective treatment for malaria symptoms, thereby abandoning the first-line treatment for malaria, artemisinin-based combination therapy (ACT). Conclusions: The most effective treatment for neonatal malaria is a combination of zinc and ORS, although less than half of children in Africa have access to ORS. Therefore, the findings of this study may encourage African countries to prioritize co-pack therapy in their procurement and supply, healthcare provider training, and expenditures. This therapy will also help alleviate the symptoms of malaria in neonates. [ABSTRACT FROM AUTHOR]

(1) Background: Prenatal depression, maternal anxiety, puerperal psychosis, and suicidal thoughts affect child welfare and development and maternal health and mortality. Women in low-income countries suffer maternal mental health issues in 25% of cases during pregnancy and 20% of cases thereafter. However, MMH screening, diagnosis, and reporting are lacking. The primary goals of the present study are twofold, as follows: firstly, to evaluate the importance of screening maternal mental health to alleviate perinatal depression and maternal anxiety, and, secondly, to analyze research patterns and propose novel approaches and procedures to bridge the current research gap and aid practitioners in enhancing the quality of care offered to women exhibiting symptoms of perinatal depression. (2) Methods: We conducted a bibliometric analysis to analyze the research topic, using the bibliometric tools Biblioshiny and VOSviewer, as well as the R statistical programming language. To accomplish our goal, we obtained a total of 243 documents from the Scopus and PubMed databases and conducted an analysis utilizing network, co-occurrence, and multiple correlation approaches. (3) Results: Most of the publications in the field were published between the years 2021 and 2024. The results of this study highlight the significance of shifting from conventional screening methods to digital ones for healthcare professionals to effectively manage the symptoms of maternal mental health associated with postpartum depression. Furthermore, the results of the present study suggest that digital screening can prevent maternal physical morbidity, contribute to psychosocial functioning, and enhance infant physical and cognitive health. (4) Conclusions: The research indicates that it is crucial to adopt and include a computerized screening practice to efficiently and immediately detect and clarify the signs of prenatal to neonatal depression. The introduction of digital screening has led to a decrease in scoring errors, an improvement in screening effectiveness, a decrease in administration times, the creation of clinical and patient reports, and the initiation of referrals for anxiety and depression therapy. [ABSTRACT FROM AUTHOR]

This research conducted a thorough investigation of Bitcoin volatility patterns using three interrelated methodologies: R/S investigation, simple moving average (SMA), and the relative strength index (RSI). The paper jointly employes the above techniques on volatility range-based estimators to effectively capture the unpredictable volatility patterns of Bitcoin. R/S analysis, SMA, and RSI calculations assess time series data obtained from our volatility estimators. Although Bitcoin is known for its high volatility and price instability, our analysis using R/S analysis and moving averages suggests the existence of underlying patterns. The estimated Hurst exponents for our volatility estimators indicate a level of persistence in these patterns, with some estimators displaying more persistence than others. This persistence underscores the potential of momentum-based trading strategies, reinforcing the expectation of additional price rises after declines and vice versa. However, significant volatility often interrupts this upward movement. The SMA analysis also demonstrates Bitcoin's susceptibility to external market forces. These observations indicate that traders and investors should modify their risk management approaches in accordance with market circumstances, perhaps integrating a combination of momentum-based and mean-reversion tactics to reduce the risks linked to Bitcoin's volatility. Furthermore, the existence of robust patterns, as demonstrated by our investigation, presents promising opportunities for investing in Bitcoin. [ABSTRACT FROM AUTHOR]

This paper provides a computationally efficient and novel four-step methodological approach for predicting volatility estimators derived from bitcoin prices. In the first step, open, high, low, and close bitcoin prices are transformed into volatility estimators using Brownian motion assumptions and logarithmic transformations. The second step determines the optimal number of time-series lags required for converting the series into an autoregressive model. This selection process utilizes random forest regression, evaluating the importance of each lag using the Mean Decrease in Impurity (MDI) criterion and optimizing the number of lags considering an 85% cumulative importance threshold. The third step of the developed methodological approach fits the Elastic Net Regression (ENR) to the volatility estimator's dataset, while the final fourth step assesses the predictive accuracy of ENR, compared to decision tree (DTR), random forest (RFR), and support vector regression (SVR). The results reveal that the ENR prevails in its predictive accuracy for open and close prices, as these prices may be linear and less susceptible to sudden, non-linear shifts typically seen during trading hours. On the other hand, SVR prevails for high and low prices as these prices often experience spikes and drops driven by transient news and intra-day market sentiments, forming complex patterns that do not align well with linear modelling. [ABSTRACT FROM AUTHOR]

LINKED CONTENT: This article is linked to Idalsoaga et al papers. To view these articles, visit https://doi.org/10.1111/apt.18193 and https://doi.org/10.1111/apt.18246 [ABSTRACT FROM AUTHOR]